Device and method for recognizing gestures for a user control interface

Abstract

In the context of a user interface control, a gesture-recognition device: receives gyroscopic data representing a gesture executed with a dedicated instrument including a gyroscopic sensor; determines a correlation between the received gyroscopic data and gyroscopic data relating to a supervised learning and pre-recorded into a database; recognizes or not the executed gesture according to said correlation, the only data representing the executed gesture taken into account being said gyroscopic data; transposes each recognized gesture into a user interface command.

The invention claimed is: 
     
       1. A gesture-recognition method for recognizing gestures in the context of user interface control, said method being implemented by a gesture-recognition device, the method comprising:
 receiving gyroscopic data representing a gesture executed with a dedicated instrument comprising a gyroscopic sensor, wherein the gesture recognition device includes a network of artificial neurons comprising output neurons associated respectively with the gestures that said device is adapted for recognizing; 
 determining, by the network of artificial neurons, a correlation between the received gyroscopic data and gyroscopic data relating to a supervised learning and pre-recorded in a database, the network of artificial neurons implementing an activation function of the sigmoid type and, for the received gyroscopic data and for each output neuron, supplying a probability that said received gyroscopic data correspond to the gesture associated with said output neuron; 
 recognizing or not the executed gesture according to said correlation, the only data representing the executed gesture taken into account being said gyroscopic data; 
 transposing each recognized gesture into a user interface command, and 
 wherein, after receiving the gyroscopic data but prior to the determination of the correlation, performing a sampling operation by equally distributed deletion or equally distributed preservation of gyroscopic data among the received gyroscopic data so that only a number of gyroscopic data equal to a number of input neurons of the network of artificial neurons is obtained; 
 
       wherein, the gyroscopic data received represent measurements made by the gyroscopic sensor on three axes of an inertial reference frame, said gesture-recognition method further comprising normalizing the received gyroscopic data so that for each original gyroscopic data item defined with respect to a given axis, the normalized gyroscopic data item is equal to a first value divided by a second value, where the first value is said original gyroscopic data item minus a minimum value of all the original gyroscopic data defined with respect to said given axis. 
     
     
       2. The gesture-recognition method according to  claim 1 , wherein, the received gyroscopic data representing measurements made by the gyroscopic sensor between two actions detected on a button of said dedicated instrument, receiving the gyroscopic-data is followed by deleting a predefined number of gyroscopic data from the start of the measurements and/or a predefined number of gyroscopic data to the end of the measurements. 
     
     
       3. The gesture-recognition method according to  claim 1 , wherein the gyroscopic data relating to the supervised learning and pre-recorded in the database correspond to all the following gestures:
 an horizontal gesture to the right ( 401 ); 
 an horizontal gesture to the left ( 402 ); 
 a vertical gesture upwards ( 403 ); and 
 a vertical gesture downwards ( 404 ). 
 
     
     
       4. The gesture-recognition method according to  claim 3 , wherein the second value is a maximum value of all the original gyroscopic data defined with respect to said given axis minus the minimum value of all the original gyroscopic data defined with respect to said given axis. 
     
     
       5. The gesture-recognition method according to  claim 3 , wherein set of gestures further comprises:
 a gesture corresponding to the reproduction of the letter “V”; and 
 a circular gesture starting from a high position and beginning the rotation towards the left. 
 
     
     
       6. The gesture-recognition method according to  claim 5 , wherein, the received gyroscopic data representing measurements made by the gyroscopic sensor on three axes of an inertial reference frame, said gesture-recognition method comprises normalizing the received gyroscopic received so that:
 for each original gyroscopic data item defined with respect to a given axis, the normalized gyroscopic data item is equal to said original gyroscopic data item minus the minimum value of all the original gyroscopic data defined with respect to said given axis, the whole divided by the minimum value of all the original gyroscopic data defined with respect to said given axis minus the mean of all the original gyroscopic data defined with respect to said given axis, the whole divided by the standard deviation of all the original gyroscopic data defined with respect to said given axis. 
 
     
     
       7. A gesture-recognition device configured for receiving gyroscopic data representing a gesture executed with a dedicated instrument comprising a gyroscopic sensor, wherein said gesture-recognition device comprises:
 a network of artificial neurons comprising output neurons associated respectively with the gestures that said device is adapted for recognizing; and 
 circuitry adapted for: 
 due to the network of artificial neurons, determining a correlation between the gyroscopic data received and gyroscopic data relating to a supervised learning and pre-recorded in a database, the network of artificial neurons being adapted for implementing an activation function of the sigmoid type and, for the gyroscopic data received and for each output neuron, supplying a probability that said received gyroscopic data correspond to the gesture associated with said output neuron; 
 recognizing or not the executed gesture according to said correlation, the only data representing the executed gesture taken into account being said gyroscopic data; 
 transposing each recognized gesture into a user interface command; and 
 wherein, after receiving the gyroscopic data but prior to the determination of the correlation, performing a sampling operation by equally distributed deletion or equally distributed preservation of gyroscopic data among the received gyroscopic data so that only a number of gyroscopic data equal to a number of input neurons of the network of artificial neurons is obtained, wherein, the gyroscopic data received represent measurements made by the gyroscopic sensor on three axes of an inertial reference frame, said circuitry is further adapted for normalizing the received gyroscopic data so that for each original gyroscopic data item defined with respect to a given axis, the normalized gyroscopic data item is equal to a first value divided by a second value, where the first value is said original gyroscopic data item minus a minimum value of all the original gyroscopic data defined with respect to said given axis. 
 
     
     
       8. The gesture-recognition device according to  claim 7 , wherein the second value is a maximum value of all the original gyroscopic data defined with respect to said given axis minus the minimum value of all the original gyroscopic data defined with respect to said given axis. 
     
     
       9. A non-transitory information storage medium storing a computer program comprising program code instructions that are configured to be loaded in a programmable device to cause said programmable device to implement the method according to  claim 1 , when the program code instructions are run by the programmable device.